New Method of the Fast Narrow Brand C-V Level Set Model for Image Segmentation
一种新的窄带快速区域水平集C-V模型图像分割方法

Region level set model for image segmentation presented by Chan-Vese can naturall change his topology with level set evolution. So level set model is widely applied into many areas, especially in image segmentation area and target trace area. Level set C-V model is better performance than level set based gradient in the anti-noise. But the evolution of C-V level set function is also more complex,and its main drawback is that evolution is relatively slow speed in particular, so this model can not be applied into practical project. Aiming at this problems, a narrow brand level set model based on region level set without Re_initialization was presented. I}his optimization method detects the approximate edge in low Resolution image, then maps this edge to the high resolution image. More accurate edges are detected in the narrow brand at the middle of the edge. The speed of edge detection is greatly improved. Finally, the feasibility of the method is validated by practical application with SAR(Synthetic Aperture Radar) image.